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This research presents an improved real-time face recognition system at a lowresolution of 15 pixels with pose and emotion and resolution variations. Wehave designed our datasets named LRD200 and LRD100, which have been used fortraining and classification. The face detection part uses the Viola-Jonesalgorithm, and the face recognition part receives the face image from the facedetection part to process it using the Local Binary Pattern Histogram (LBPH)algorithm with preprocessing using contrast limited adaptive histogramequalization (CLAHE) and face alignment. The face database in this system canbe updated via our custom-built standalone android app and automatic restartingof the training and recognition process with an updated database. Using ourproposed algorithm, a real-time face recognition accuracy of 78.40% at 15 pxand 98.05% at 45 px have been achieved using the LRD200 database containing 200images per person. With 100 images per person in the database (LRD100) theachieved accuracies are 60.60% at 15 px and 95% at 45 px respectively. A facialdeflection of about 30 degrees on either side from the front face showed anaverage face recognition precision of 72.25% - 81.85%. This face recognitionsystem can be employed for law enforcement purposes, where the surveillancecamera captures a low-resolution image because of the distance of a person fromthe camera. It can also be used as a surveillance system in airports, busstations, etc., to reduce the risk of possible criminal threats.

An Improved Real-Time Face Recognition System at Low Resolution Based on Local Binary Pattern Histogram Algorithm and CLAHE